Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:7450-7454. doi: 10.1109/EMBC46164.2021.9629949.
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of human mortality worldwide. Traditionally, estimating COPD severity has been done in controlled clinical conditions using cough sounds, respiration, and heart rate variability, with the latter reporting insights on the autonomic dysfunction caused by the disease. Advancements in remote monitoring and wearable device technologies, in turn, have allowed for remote COPD monitoring in daily life conditions. In this study, we explore the potential for predicting COPD severity and exacerbation using a low-cost wearable device that measures heart rate and activity data. We collected smartwatch sensor data from 35 COPD patients over a period of three months. Our evaluation shows that future trajectory of the disease can be predicted using only the first few days of continuous unobtrusive wearable data collected from COPD patients. Using features extracted from wearable device an Isolation Forest was able to predict exacerbation with an area under curve (AUC) 0.69 thus showing improvement over a random choice classifier.
慢性阻塞性肺疾病（COPD）是全球导致人类死亡的主要原因之一。传统上，COPD 严重程度的评估是在受控的临床条件下通过咳嗽声音、呼吸和心率变异性来完成的，后者可以报告疾病引起的自主功能障碍的相关信息。远程监测和可穿戴设备技术的进步，反过来又允许在日常生活条件下进行远程 COPD 监测。在这项研究中，我们探索了使用低成本可穿戴设备测量心率和活动数据来预测 COPD 严重程度和加重的可能性。我们从 35 名 COPD 患者那里收集了智能手表传感器数据，为期三个月。我们的评估表明，仅使用从 COPD 患者收集的连续几天无干扰的可穿戴数据的前几天就可以预测疾病的未来轨迹。使用从可穿戴设备中提取的特征，隔离森林能够以 0.69 的曲线下面积（AUC）预测加重，因此相对于随机选择分类器有所改善。
